European Stroke Organisation and European Academy of Neurology joint guidelines on post‐stroke cognitive impairment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The optimal management of post-stroke cognitive impairment (PSCI) remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making regarding prevention, diagnosis, treatment and prognosis. METHODS: Guidelines were developed according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews, assessed the quality of the available evidence, and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations. RESULTS: There was limited randomized controlled trial (RCT) evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Lifestyle interventions and treating vascular risk factors have many health benefits, but a cognitive effect is not proven. We found no evidence regarding routine cognitive screening following stroke, but recognize the importance of targeted cognitive assessment. We describe the accuracy of various cognitive screening tests, but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognition. The association between PSCI and acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on brain magnetic resonance imaging may help predict cognitive outcomes. CONCLUSIONS: These guidelines highlight fundamental areas where robust evidence is lacking. Further definitive RCTs are needed, and we suggest priority areas for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".